1. Field of the Invention
The invention relates to a method for mapping an original image from a source gamut color subspace to a destination gamut color subspace including first filtering the original image to obtain a first intermediate image; gamut mapping the first intermediate image to obtain a second intermediate image; second filtering the first intermediate image to obtain a third intermediate image; and merging the second intermediate image and the third intermediate image to obtain a target image. The invention also relates to an image display system and a color image printing system adapted to implement the method.
2. Description of Background Art
Gamut Mapping Algorithms (GMA's) are used to manage the color gamut changes between an original image and its reproduction via a given technology like printing, photographic imaging, electronic displaying, etc. These changes correspond to shape differences and size reduction of the gamut causing a loss of information. Ideally, a GMA should optimize the reproduction by taking into account the color and spatial distribution of the original image, such that the reproduction is perceived as similar as possible to the original. In the quest for an optimal reproduction, an impressive number of GMAs have been proposed in the literature. In “The fundamentals of gamut mapping: A survey” of J. Morovic and R. Luo an exhaustive survey has been presented. The ICC color management flow is based on the first generation, non-adaptive point-wise GMAs. Morovic classified these classic GMAs into two categories: gamut clipping and gamut compression. Gamut clipping algorithms project color lying outside the output gamut into its boundary. They usually preserve saturation, but clip image details, and introduce clipping artefacts. Gamut compression algorithms compress the input gamut onto the output gamut and are better at preserving details but tend to reduce saturation.
In “Digital Colour Imaging Handbook, Chapter 10: Gamut Mapping” of J. Morovic, adaptive algorithms with the selection of an appropriate GMA depending on the image type or directly on the image gamut instead of the input device gamut have been investigated. To further improve adaptive GMAs, it has been advocated that preservation of the spatial details in an image is a very important issue for perceptual quality. GMAs adaptive to the spatial content of the image, i.e. Spatial Gamut Mapping Algorithms (SGMAs), have been introduced. These algorithms try to balance both color accuracy and preservation of details, by acting locally to generate a reproduction perceived as close to the original. Two families of SGMAs which follow different approaches may be distinguished: the first uses iterative optimization tools, the second reinserts high-frequency content in clipped images to compensate for the loss of details caused by clipping. The optimization family includes algorithms proposed in “Colour gamut mapping based on a perceptual image difference measure” by Nakauchi et al. Using models of perception of the Human Visual System (HVS), the algorithms minimize the perceived differences between the original and the candidate reproduction by locally modifying the candidate. In these optimization loops, the main difficulty is to define an appropriate criterion to optimize, using a valid perceptual model. Another problem is the lengthy computing time, making these algorithms difficult to use in an industrial context. Algorithms of the second family are usually sufficiently fast to be implemented in an industrial color flow. They have a less ambitious motivation: to limit or compensate for the loss of details caused by clipping algorithms. Clipping yields good results in terms of saturation but tend to degrade image details in saturated areas. The projection might fail because it projects all non reproducible colors lying on the line of the projecting direction onto the same point on the gamut boundary. If in a local area, several neighboring pixels lie on the same line of projection but with distinguishable colors, the local variations that form the spatial content will be erased. Similarly, if pixels in a local neighborhood lie on nearby projection lines, they will be mapped to nearby points on the gamut hull, and the local spatial variations may be severely diminished. To prevent these degradations, for this second family of SGMAs improvements have already been proposed in the art. These improvements proposed so far can be divided in two groups.
In the first group (XSGM), as disclosed in “Gamut mapping to preserve spatial luminance variations” by Balasubramanian et al., the original image is gamut mapped using a direction of projection that emphasises preservation of chroma over luminance. The parts of the original image that were clipped are high pass filtered and added to the gamut mapped image. The resulting sum is again gamut mapped using a direction of projection that emphasises preservation of luminance over chroma. Previously conducted psycho-physical evaluations showed that this method obtains good scores but suffers from the presence of halos.
In the second group, as disclosed i.e. in “A multi-resolution, full color spatial gamut mapping algorithm” by Morovic and Wang, it is proposed to first decompose the image in frequency bands. The low pass band is gamut mapped then successive clippings are performed during the reconstruction. Results of such an approach depend both on the algorithm used in the image decomposition and on the GMAs successively applied.
In both groups, problems may arise when adding high pass content to the gamut mapped image: artefacts such as halos and color shifts might be introduced.
In order to further mitigate the problems of the prior art an improved Gamut Mapping Algorithm is proposed.